Multiple-scale quantum harmonic oscillator multi-mode function optimization system and method

Abstract

The invention relates to the calculation intelligent field and particularly relates to a multiple-scale quantum harmonic oscillator multi-mode function optimization system and a method. The invention improves the optimal position selection strategy of the current MQHOA method, all gauss sampling areas perform comparison on function values by targeting the sampling points generated by itself and the position of the optimal value is reserved as a new gauss sampling center. In the meantime, the convergence condition of the innermost loop is changed to the condition where the difference of the variance of all gauss sampling center positions between two iterations is smaller or equal to the current scale, and the QHO (quantum harmonic oscillator iteration ) performs internal layer circulation convergence. In the invention, the essence of the QHO convergence condition is to perform convergence when the change of each gauss sampling area position is small. The invention can realize the optimization problem of the complex function of the multi-global optimal position. For the complex functions having the multi-global optimal position, the method disclosed by the invention can perform convergence in most of time and thus realizes the search for the multiple global optimal positions.

Description

Technical field [0001] The invention relates to the field of computational intelligence, and in particular to a system and method for optimizing multi-scale quantum harmonic oscillators and multi-mode functions. The system and method can be widely used in the fields of industry, economy, science and the like. Background technique [0002] The multi-scale quantum harmonic oscillator optimization method (Multi-scale Quantum Harmonic Oscillator Algorithm, MQHOA) is a computationally intelligent method to solve the unimodal global optimization problem constructed by the probability interpretation of the quantum harmonic oscillator wave function. [0003] The MQHOA method is designed to aim at the unimodal function optimization problem. When the objective function has multiple global optimal positions, the existing MQHOA method is prone to failure, that is, the existing MQHOA method can only achieve a global optimal position. Optimization of complex functions. Moreover, due to the limi...

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Problems solved by technology

[0003] The MQHOA method is designed with the goal of unimodal function optimization. When the objective function has multiple global optimal positions, the existing MQHOA method is prone to failure, that is, the existing MQHOA method can only achieve one global optimal position. Optimization problems of complex functions

Moreover, due to the limitation of the convergence condition of the inner loop (QHO loop, QuantumHarmonicOscillator) of MQHOA, for multimodal complex functions with multiple global optimal positions, this method will not be able to converge most of the time, so that it cannot achieve multiple global optimal positions. search for optimal location

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